FaceFolds: Meshed Radiance Manifolds for Efficient Volumetric Rendering of Dynamic Faces

TL;DR

FaceFolds introduces a novel neural rendering method for dynamic faces that achieves high-quality, efficient volumetric rendering with minimal resources, compatible with standard graphics hardware, and suitable for real-time applications.

Contribution

The paper presents a new representation using neural radiance manifolds for efficient, high-quality volumetric rendering of dynamic faces, enabling compatibility with legacy graphics systems.

Findings

Achieves photorealistic rendering comparable to state-of-the-art methods.

Runs efficiently on commodity hardware with minimal compute and memory.

Supports real-time rendering in game engines.

Abstract

3D rendering of dynamic face captures is a challenging problem, and it demands improvements on several fronts$\unicode{x2014}$photorealism, efficiency, compatibility, and configurability. We present a novel representation that enables high-quality volumetric rendering of an actor's dynamic facial performances with minimal compute and memory footprint. It runs natively on commodity graphics soft- and hardware, and allows for a graceful trade-off between quality and efficiency. Our method utilizes recent advances in neural rendering, particularly learning discrete radiance manifolds to sparsely sample the scene to model volumetric effects. We achieve efficient modeling by learning a single set of manifolds for the entire dynamic sequence, while implicitly modeling appearance changes as temporal canonical texture. We export a single layered mesh and view-independent RGBA texture video that is compatible with legacy graphics renderers without additional ML integration. We demonstrate our method by rendering dynamic face captures of real actors in a game engine, at comparable photorealism to state-of-the-art neural rendering techniques at previously unseen frame rates.